估计员
核密度估计
风力发电
核(代数)
风电预测
分位数
计算机科学
核回归
风速
数学优化
概率预测
分位数回归
计量经济学
功率(物理)
数学
电力系统
机器学习
人工智能
工程类
统计
气象学
地理
物理
电气工程
量子力学
概率逻辑
组合数学
作者
Ricardo Bessa,Vladimiro Miranda,Audun Botterud,Jianhui Wang,Emil M. Constantinescu
出处
期刊:IEEE Transactions on Sustainable Energy
[Institute of Electrical and Electronics Engineers]
日期:2012-10-01
卷期号:3 (4): 660-669
被引量:152
标识
DOI:10.1109/tste.2012.2200302
摘要
This paper reports the application of a new kernel density estimation model based on the Nadaraya-Watson estimator, for the problem of wind power uncertainty forecasting. The new model is described, including the use of kernels specific to the wind power problem. A novel time-adaptive approach is presented. The quality of the new model is benchmarked against a splines quantile regression model currently in use in the industry. The case studies refer to two distinct wind farms in the United States and show that the new model produces better results, evaluated with suitable quality metrics such as calibration, sharpness, and skill score.
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